Communications with guaranteed bandwidth and low latency using frequency-referenced multiplexing

Emerging cloud applications such as virtual reality and connected car fleets demand guaranteed connections, as well as low and stable latency, to edge data centres. Currently, user–cloud communications rely on time-scheduled data frames through tree-topology fibre networks, which are incapable of providing guaranteed connections with low or stable latency and cannot be scaled to a larger number of users. Here we show that a frequency-referenced multiplexing method can provide guaranteed bandwidth and low latency for time-critical applications. We use clock and optical frequency synchronization, enabled by frequency comb and signal processing techniques, to provide each user with dedicated optical bandwidth, creating scalable user–cloud upstream communications. As a proof of concept, we demonstrate a frequency-division multiplexing system servicing up to 64 users with an aggregate bandwidth of 160 GHz, exhibiting a data rate of up to 4.3 Gbps per user (240.0 Gbps aggregated capacity considering a 200 GHz wavelength band) with a high receiver sensitivity of –35 dBm

LunaNet: a Flexible and Extensible Lunar Exploration Communications and Navigation Infrastructure

NASA has set the ambitious goal of establishing a sustainable human presence on the Moon. Diverse commercial and international partners are engaged in this effort to catalyze scientific discovery, lunar resource utilization and economic development on both the Earth and at the Moon. Lunar development will serve as a critical proving ground for deeper exploration into the solar system. Space communications and navigation infrastructure will play an integral part in realizing this goal. This paper provides a high-level description of an extensible and scalable lunar communications and navigation architecture, known as LunaNet. LunaNet is a services network to enable lunar operations. Three LunaNet service types are defined: networking services, position, navigation and timing services, and science utilization services. The LunaNet architecture encompasses a wide variety of topology implementations, including surface and orbiting provider nodes. In this paper several systems engineering considerations within the service architecture are highlighted. Additionally, several alternative LunaNet instantiations are presented. Extensibility of the LunaNet architecture to the solar system internet is discussed

Cognitive radio-enabled Internet of Vehicles (IoVs): a cooperative spectrum sensing and allocation for vehicular communication

Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75MHz spectrum in the 5.9GHz band to support vehicular communication which many studies have found insufficient. In this paper, we studied the application of Cognitive Radio (CR) technology to IoVs in order to increase the spectrum resource opportunities available for vehicular communication, especially when the officially allocated 75MHz spectrum in 5.9GHz band is not enough due to high demands as a result of increasing number of connected vehicles as already foreseen in the near era of IoTs. We proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum bands on the highways. We also developed a novel co-operative three-state spectrum sensing and allocation model which makes CR vehicular secondary units (SUs) aware of additional spectrum resources opportunities on their current and future positions and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing and provide vehicles with additional spectrum opportunities without harmful interference against the Primary Users (PUs) activities

Indoor Positioning Trends in 5G-Advanced: Challenges and Solution towards Centimeter-level Accuracy

After robust connectivity, precise positioning is evolving into an innovative component of 5G service offerings for industrial use-cases and verticals with challenging indoor radio environments. In this direction, the 3GPP Rel-16 standard has been a tipping point in specifying critical innovations, followed by enhancements in Rel-17+. In this article, we follow this path to elaborate on the 5G positioning framework, measurements, and methods before shifting the focus to carrier-phase (CP) measurements as a complementary measure for time- and angular-based positioning methods toward achieving centimeter-level accuracy. As this path is not without challenges, we discuss these and outline potential solutions. As an example of solutions, we study how phase-continuous reference signaling can counter noisy phase measurements using realistic simulations in an indoor factory (InF) scenario.Comment: 5 figures, 1 table, under review for possible publication in IEEE Communications Magazin

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Benets of tight coupled architectures for the integration of GNSS receiver and Vanet transceiver

Vehicular adhoc networks (VANETs) are one emerging type of networks that will enable a broad range of applications such as public safety, traffic management, traveler information support and entertain ment. Whether wireless access may be asynchronous or synchronous (respectively as in the upcoming IEEE 8021.11p standard or in some alternative emerging solutions), a synchronization among nodes is required. Moreover, the information on position is needed to let vehicular services work and to correctly forward the messages. As a result, timing and positioning are a strong prerequisite of VANETs. Also the diffusion of enhanced GNSS Navigators paves the way to the integration between GNSS receivers and VANET transceiv ers. This position paper presents an analysis on potential benefits coming from a tightcoupling between the two: the dissertation is meant to show to what extent Intelligent Transportation System (ITS) services could benefit from the proposed architectur

Radio Frequency Emitter Geolocation Using Cubesats

The ability to locate an RF transmitter is a topic of growing interest for civilian and military users alike. Geolocation can provide critical information for the intelligence community, search and rescue operators, and the warfighter. The technology required for geolocation has steadily improved over the past several decades, allowing better performance at longer baseline distances between transmitter and receiver. The expansion of geolocation missions from aircraft to spacecraft has necessitated research into how emerging geolocation methods perform as baseline distances are increased beyond what was previously considered. The CubeSat architecture is a relatively new satellite form which could enable small-scale, low-cost solutions to USAF geolocation needs. This research proposes to use CubeSats as a vehicle to perform geolocation missions in the space domain. The CubeSat form factor considered is a 6-unit architecture that allows for 6000 cm3 of space for hardware. There are a number of methods which have been developed for geolocation applications. This research compares four methods with various sensor configurations and signal properties. The four methods\u27 performance are assessed by simulating and modeling the environment, signals, and geolocation algorithms using MATLAB. The simulations created and run in this research show that the angle of arrival method outperforms the instantaneous received frequency method, especially at higher SNR values. These two methods are possible for single and dual satellite architectures. When three or more satellites are available, the direct position determination method outperforms the three other considered methods

Optimal Precoders for Tracking the AoD and AoA of a mm-Wave Path

In millimeter-wave channels, most of the received energy is carried by a few paths. Traditional precoders sweep the angle-of-departure (AoD) and angle-of-arrival (AoA) space with directional precoders to identify directions with largest power. Such precoders are heuristic and lead to sub-optimal AoD/AoA estimation. We derive optimal precoders, minimizing the Cram\'{e}r-Rao bound (CRB) of the AoD/AoA, assuming a fully digital architecture at the transmitter and spatial filtering of a single path. The precoders are found by solving a suitable convex optimization problem. We demonstrate that the accuracy can be improved by at least a factor of two over traditional precoders, and show that there is an optimal number of distinct precoders beyond which the CRB does not improve.Comment: Resubmission to IEEE Trans. on Signal Processing. 12 pages and 9 figure

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system